Journal ArticleDOI
Knowledge-based system for rostering
K.P. Chow,C.K. Hui +1 more
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TLDR
A case study on designing and implementing a computer rostering system for a group of airport servicing staffs using knowledge-based approach and the system has been rewritten in C recently so that the knowledge- based system can communicate with other systems in the organization and has a better performance.Abstract:
Scheduling problems are commonly found in servicing and manufacturing industries. In order to maximize the utilization of the supporting hardware and machines, machine operators usually are required to work on shift so that machines remain running continuously without stopping. The construction of the shift duty assignment, usually called roster, is a nontrivial task since many factors need to be considered, such as the maximum number of hours an operator can work continuously and the minimum rest time between two consecutive duties. Since the construction of roster requires nontrivial intellectual activities, it is difficult to computerize roster scheduling using conventional data processing techniques. On the other hand, such roster planning process has been performed by the human planner for years, and experience has been collected to perform the planning task efficiently. Knowledge-based approach takes advantage of human expertise and thus is used to automate the roster scheduling activities. We report here a case study on designing and implementing a computer rostering system for a group of airport servicing staffs using knowledge-based approach. Prototyping is used in the development process. Knowledge is represented in two different forms, namely forward chaining rules and constraints. Forward chaining and iterative improvement are used as control strategies in the system. The first prototype was completed in less than a month. During the refinement process, a tool, called SHO, was developed to simplify the modification process. The whole system was completed in approximately 6 months, of which most of the time was spent on knowledge and rules refinement. Initial implementation was developed using Common Lisp on a Sun Workstation. It is then ported to PC Scheme, a Lisp dialect, on an IBM Personal Computer. The system has been used for rostering up to 500 airport servicing staffs for a few years. As most knowledge and rules have been incorporated into the system, the system has been rewritten in C recently so that the knowledge-based system can communicate with other systems in the organization and has a better performance.read more
Citations
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Journal ArticleDOI
An Annotated Bibliography of Personnel Scheduling and Rostering
TL;DR: This annotated bibliography puts together a comprehensive collection of some 700 references in this area, focusing mainly on algorithms for generating rosters and personnel schedules but also covering related areas such as workforce planning and estimating staffing requirements.
Journal ArticleDOI
Nurse scheduling with tabu search and strategic oscillation
TL;DR: The problem of producing rosters for nursing staff in a large general hospital is tackled using tabu search with strategic oscillation, a robust and effective method which is able to match the quality of solutions produced by a human expert.
Journal ArticleDOI
Editorial: AI planning and scheduling in the medical hospital environment
TL;DR: An overview of the achievements reported in therapy planning and hospital management together with a general roadmap of the published research in Artificial Intelligence planning and scheduling is presented.
Journal ArticleDOI
Railway track possession assignment using constraint satisfaction
TL;DR: A case study on applying constraint-satisfaction techniques to solve a real-world resource allocation problem, referred to as the Railway Track Possession Assignment Problem, using the CHIP constraint language, which succeeded in replacing the manual assignment process.
Journal ArticleDOI
A neural network application in personnel scheduling
Gang Hao,Kin Keung Lai,M. Tan +2 more
TL;DR: A neural network model following a relatively new modeling approach to solve a real rostering case is introduced and the experiment results comparing the neural network method with three popular heuristics, i.e. simulated annealing, Tabu search and genetic algorithm are provided.
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